Online Review Helpfulness: Role of Qualitative Factors

Consumers are increasingly reading online reviews before making any purchasing decisions. The significance of online reviews has only grown over the years. Though in the past, scholars have emphasized the impact of quantitative factors (e.g., review ratings) on online reviews, only recently have they begun to explore the role of qualitative aspects of online reviews. Content readability and associated sentiments in text provide two important qualitative cues that influence the helpfulness of online reviews. However, the extant literature has overemphasized the linear association between these aspects and the helpfulness of reviews. Using the elaboration likelihood model and the classic ideal point concept, the current work asserts that after an ideal point is attained, lucid and sentimental reviews diminish in utility (i.e., helpfulness of an online review for consumers decreases). This may happen because consumers are wary of fraudulent reviews. This study proposes that if experienced reviewers give such extreme reviews, then consumers might still draw utility from these reviews. In other words, this study explains the moderating role of reviewer experience, which heuristically influences consumers’ trust of online reviews, thus making even too simplistic or extremely sentimental reviews helpful.     

Do product reviews really reduce search costs?

The literature on online product reviews is based on the fundamental premise that reviews impact search costs and also affect consumers’ confidence in their purchase decisions. However, this proposition has not been proven in the literature. To this end, we conducted an experiment using an eye-tracking machine to measure the impact of online editorial and customer reviews on consumer’s information search costs and on decision confidence. Search costs in this study are defined in terms of time costs and cognitive effort costs. We find that when present, both editorial reviews and customer reviews separately reduce both search time and cognitive effort considerably, but not when present together. We also find that the presence of both types of reviews increases decision confidence considerably, but do not lower search costs. These results suggest that ecommerce firms can benefit from the presence of either or both editorial and customer reviews through either lower search costs, or higher decision confidence. We conclude with several managerial recommendations for ecommerce firms.     

Selling to Millennials with online reviews

Millennials have heavily influenced social media's evolution into an important source of product information. They are increasingly basing their product evaluations on information gathered from online reviews. Thus, companies targeting Millennials may wish to pay heed to online reviews. Which products are affected by Millennials’ online reviews? Do the reviews tend to be positive or negative? Where do Millennial customers look for information amongst the plethora of online venues? As presented herein, a survey of 227 Millennials reveals that this generation is undeniably posting reviews online and being influenced by these reviews. Respondents show a definite preference for two online venues, Facebook and company websites, when voicing their opinions. Reviews are broken down by positive and negative comments, and how product categories fare for each type. Contrary to popular thinking, the respondents were more prone to post positive reviews than negative reviews. Males voice their opinions online significantly more often than females, and specific gender differences are observed by product category. Recommendations are provided for selling to Millennials by leveraging online reviews.     

Unveiling user-generated content: Designing websites to best present customer reviews

Online customer reviews are an important type of user-generated content, through which consumers share their experiences with products and services in order to help others make informed purchasing decisions. In this article, we discuss the goals of online customer reviews and develop practical, actionable principles that can be used to improve the presentations of customer reviews. We identify four goals—two ultimate, and two intermediate—of online customer reviews. The two ultimate goals are: (1) to assist consumers in making accurate choices, and (2) to reduce the cognitive costs of making such choices. The two intermediate goals are: (1) to help consumers form an unbiased understanding of the product, and (2) to construct a set of evaluative criteria. Drawing on the constructive view of consumer judgment and choice, we present a conceptual model of online-review based consumer judgment and choice. We report principles that will improve the presentation of reviews, and discuss the benefits of the new design over the traditional presentation of online reviews.     

The Role of Emotions for the Perceived Usefulness in Online Customer Reviews

Online customer reviews often express emotions. This can enable marketers to analyze the textual content of online reviews with the aim to understand the role of emotions and how they can affect other customers. In this paper, we present an approach to extracting emotion content from online reviews in order to measure the importance of various emotion dimensions within different product categories. The approach uses an emotion lexicon to extract emotion terms, while it also builds a classification model to measure the importance of emotion dimensions based on the quality of reviews. Review quality is measured based on the usefulness of online customer reviews, which are perceived and evaluated by other customers through their helpfulness ratings. This approach allows the identification of emotion dimensions that characterize qualitative reviews. The empirical evaluation in our study suggests that trust, joy, and anticipation are the most decisive emotion dimensions, although substantial variance across product categories can also be detected. Additionally, we compared two contrasting emotion dictionaries. One lexicon was crowd-funded and contained a large vocabulary, whereas the other was more focused and smaller, since it was created word-wise by an expert. Our empirical findings indicate that the crowd-funded solution outperforms its smaller counterpart in terms of classification precision. The main implication of this study is that it adds an emotional perspective to the broad set of existing tools that marketers employ to analyzing online reviews. Our contributions are: i) we are the first to analyze emotions' role in online customer reviews; ii) we demonstrate how to develop a big data model such as this, without external assistance; iii) we show how to interpret the results of the created model; and iv) we show which dictionary to prefer when creating the model.     

Understanding the Impact of Online Reviews on Customer Choice: A Probability Discounting Approach

This study uses the concept of probability discounting to understand the impact of online customer reviews on consumer choice. Probability discounting describes how the subjective value of an outcome alters when its delivery shifts from certain to uncertain. An experimental study with 29 participants was conducted. Participants were run through an online shopping scenario where they had to choose whether to buy a product from a Web shop with customer reviews on reliability or from a Web shop without reviews but with a lower product price. A titration procedure over sales price for the Web shop without reviews was run over seven probability conditions. The mean switching points where participants chose where to buy the product were extracted from the experimental data, and probability discounting factors were calculated. The results supported the assumption that online reviews indicate the probability of a successful transaction online and function as a guide to choices. Implications for marketers as well as suggestions for future research are discussed.     

Creating and detecting fake reviews of online products

Customers increasingly rely on reviews for product information. However, the usefulness of online reviews is impeded by fake reviews that give an untruthful picture of product quality. Therefore, detection of fake reviews is needed. Unfortunately, so far, automatic detection has only had partial success in this challenging task. In this research, we address the creation and detection of fake reviews. First, we experiment with two language models, ULMFiT and GPT-2, to generate fake product reviews based on an Amazon e-commerce dataset. Using the better model, GPT-2, we create a dataset for a classification task of fake review detection. We show that a machine classifier can accomplish this goal near-perfectly, whereas human raters exhibit significantly lower accuracy and agreement than the tested algorithms. The model was also effective on detected human generated fake reviews. The results imply that, while fake review detection is challenging for humans, “machines can fight machines” in the task of detecting fake reviews. Our findings have implications for consumer protection, defense of firms from unfair competition, and responsibility of review platforms.     

How consumers use product reviews in the purchase decision process

This paper uses individual level data to examine the influence of product reviews in different stages of the consumer’s purchase decision process. Specifically, a two-stage model consisting of consideration set formation and choice is posited, where the consumer can incorporate information from product reviews in each stage. The model is estimated using an online panel survey about hotel choice. We find that (1) consumers use product reviews more in the consideration set stage and less in the choice stage, (2) Bayesian updating of prior perceived quality better explains how consumers use product reviews compared to two competing updating methods, and (3) the monetary value of a unit increase in the mean of product reviews is computed. Our results suggest that managers should make product review information (their number, average, and variance) available from the beginning of the search process and encourage satisfied customers to write reviews.     

Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus

Current discussions in academia and in the press increase consumers’ awareness of potentially deceptive online reviews. The increasing practice of fake reviews posted online not only jeopardizes the credibility of review sites as important information sources for individuals but also endangers a valuable source of information for service providers. Two studies shed further light on the role of consensus and identity-related information in assisting consumers detect potentially faked reviews. In one preliminary study, a sample of 4826 rejected and 4881 published online reviews was analyzed to investigate the differences in the disclosure of author-related information such as name and age as well as star ratings across those reviews. In the main study, a 3 (identity disclosure) x 2 (consensus) x 2 (priming of fake reviews) experiment was carried out with 390 respondents. The results highlight the relevance of the review's consensus in relation to the overall rating of previous reviews and corroborate the results of the preliminary study from the perspective of an internet user: the value of the amount of available information on the review's author in assisting individuals detect potential fake reviews. This study complements research in computer science by highlighting the relevance of contextual—in addition to textual—indicators that assist internet users in detecting potentially deceptive online reviews.     

The Recipe for the Perfect Review?

Online product reviews, originally intended to reduce consumers’ pre-purchase search and evaluation costs, have become so numerous that they are now themselves a source for information overload. To help consumers find high-quality reviews faster, review rankings based on consumers’ evaluations of their helpfulness were introduced. But many reviews are never evaluated and never ranked. Moreover, current helpfulness-based systems provide little or no advice to reviewers on how to write more helpful reviews. Average review quality and consumer search costs could be much improved if these issues were solved. This requires identifying the determinants of review helpfulness, which we carry out based on an adaption of Wang and Strong’s well-known data quality framework. Our empirical analysis shows that review helpfulness is influenced not only by single-review features but also by contextual factors expressing review value relative to all available reviews. Reviews for experiential goods differ systematically from reviews for utilitarian goods. Our findings, based on 27,104 reviews from Amazon.com across six product categories, form the basis for estimating preliminary helpfulness scores for unrated reviews and for developing interactive, personalized review writing support tools.     

普通消费者口碑和专家评论对消费者购买决策的影响研究

在线评论作为营销信息中新的要素，已成为当下消费者购买产品或服务时的重要因素。文章根据获得诊断性模型和调节导向理论，引入自我调节导向作为调节变量，探讨在线评论信息源对品牌评价和购买意愿的影响。文章采用情境模拟实验方法，考察了普通消费者口碑和专家评论对消费者的品牌评价和购买意愿具有不同的影响。具体来说当消费者处于促进调节导向时，普通消费者口碑比专家评论更容易使消费者产生良好的品牌评价和购买意愿；当消费者处于预防调节导向时，专家评论比普通消费者口碑更容易使消费者产生良好的品牌评价和购买意愿，其中感知诊断性在这个过程中起到中介作用。文章整合了不同领域的理论，拓宽了不同来源的在线评论对消费者影响的理解力，而且研究结论对网站的营销人员如何管理在线评论有一定的启示意义。     

Emotional and the normative aspects of customers’ reviews

The goal of the current research is to investigate the link between the emotional aspects of hotel and travel organization customers' reviews and their normative (e.g., star rating) rankings. After filtering, the Yelp dataset generated 3,47,803 hotel and travel company reviews. Following the purification of user reviews, we used an unsupervised machine learning technique-based NRC Emotion Lexicon to study the relationships between various emotional aspects of reviews and their normative values (e.g., star rating) for the review. Customers express different sorts of feelings for different types of emotional aspects, forcing them to assign different stars, according to the study's findings. The study is the first to use a lexicon-based unsupervised learning approach to look into the emotional aspects of hotel and travel organization reviews and associated normative (e.g., star rating) rankings.     

Uncovering evidence on community-based injury prevention: A review of programme effectiveness and factors influencing effectiveness

This study examines systematic reviews of community-based injury prevention programmes to obtain an overview of the evidence base on the effectiveness of these programmes and to analyse how effectiveness is measured and the extent to which factors contributing to achieving programme effectiveness are examined in these reviews. Thirteen systematic reviews were found, encompassing a total of 121 programmes. The results reinforced the well-documented point that the evidence regarding the effectiveness of community-based injury prevention programmes is inconsistent. Some of the programmes targeting specific injury categories, e.g. specific injury types and/or age groups, were successful, whilst more broadly targeted programmes demonstrated less convincing results. Effectiveness was predominantly measured as injury rate reductions. Only one of the reviews identified contextual factors that could have impacted on programme effectiveness. To advance the field, researchers and systematic reviews need to include evidence on factors that may explain how the effects were achieved.     

Taking Fake Online Consumer Reviews Seriously

Evidence discussed in this article indicates that consumers rely heavily upon consumer reviews when making decisions about which products and services to purchase online. Sellers and their marketeers are aware of this, and as a result, some of them succumb to the temptation to generate fake consumer reviews. This article argues that policymakers and regulators need to take fake reviews seriously. This is because they undermine a (potentially) effective and efficient mechanism for overcoming information asymmetry between online sellers and buyers. Consumer reviews also offer a powerful mechanism for regulating the marketplace. Sellers who sell sub-standard products or engage in sub-standard selling practices risk reputational damage. Genuine consumer reviews can therefore moderate bad seller behaviour and assist in improving the quality and efficiency of the marketplace. Although there are laws in many jurisdictions that prohibit misleading and deceptive conduct, detecting fake reviews is complex and difficult. This article proposes that one way of increasing the effectiveness of regulatory oversight is for regulators to add an “alliance approach” to their existing arsenal of regulatory systems and mechanisms.     

How managerial responses to online reviews affect customer satisfaction: An empirical study based on additional reviews

This study investigates the internality of managerial responses (MRs) to online reviews on the responded customers' (i.e., customers who have posted an initial review and then being responded by the company through an MR) satisfaction. By utilizing the data of additional reviews (ARs), an innovative social media module for customers to express follow-up opinions that complement their initial reviews, we examine the impact of MR treatment on the customers' AR valence. We leverage the insight that the observability of MR at the time of the responded customer posts an AR is a crucial condition to the impact of the MR, and thus regard the observability of MR as an exogenous treatment for model identification. Fixed effects models with extensive control variables are proposed to estimate the MR treatment effects. The results show that MRs have significant positive impacts on customers' satisfaction in ARs. Further explorations show that the positive impacts are mainly due to the positive effects of MRs on nonpositive initial reviews (MR-Ns), suggesting that MR-Ns are an effective management tool for customer complaints. Moreover, this study identifies MR delay as a boundary condition for the internality of MRs because MR delay negatively moderates the positive impacts of MRs. Therefore, companies should promptly respond to customers' negative opinions in their reviews. This study is among the very first to clearly identify the internality of MRs on the responded customers’ satisfaction. We show that the existing results on the externality of MRs are not directly applicable to the internality of MRs, highlighting the novelty of this study. The obtained new insights provide practical guidelines for companies to adjust their intervention strategies on e-commerce platforms.     

Mining the text of online consumer reviews to analyze brand image and brand positioning

The growth of the Internet has led to massive availability of online consumer reviews. So far, papers studying online reviews have mainly analysed how non-textual features, such as ratings and volume, influence different types of consumer behavior, such as information adoption decisions or product choices. However, little attention has been paid to examining the textual aspects of online reviews in order to study brand image and brand positioning. The text analysis of online reviews inevitably raises the concept of “text mining”; that is, the process of extracting useful and meaningful information from unstructured text. This research proposes an unified, structured and easy-to-implement procedure for the text analysis of online reviews with the ultimate goal of studying brand image and brand positioning. The text mining analysis is based on a lexicon-based approach, the Linguistic Inquiry and Word Count (Pennebaker et al., 2007), which provides the researcher with insights into emotional and psychological brand associations.     

TOOR: A Novel Product Title Optimization Method Based on Online Reviews in E-commerce

Titles of online products play an important role in attracting consumers and promoting product sales in e-commerce. However, current online product titles only cover basic features and cannot reflect the preferences of consumers exactly. To address this problem, this research proposed an online title optimization method based on the analysis of online reviews, which is called TOOR (Title Optimization based on Online Reviews). In this research, we analyzed and compared product features extracted from online product titles and online reviews from the point of view of consumers and applied features extracted from reviews to title optimization. In order to verify the effectiveness of the proposed method, two experiments were conducted in this paper, selecting four typical smartphones as experiment samples and Taobao.com as the data resources. The experimental results indicated that features extracted from online reviews can better reflect the consumers’ concern, and the titles optimized by the TOOR method are more appealing to consumers and have higher click-through rates.     

Comparing consumer-produced product reviews across multiple websites with sentiment classification

ABSTRACT

Online consumer reviews have been extensively studied. However, existing literature analyzing online consumer review data mostly relies on a single data source, resulting in potentially biased analytics conclusions. Many websites encourage consumers to post reviews of their purchased products, so that new consumers can evaluate these reviews for the same product across different websites to help them make purchasing decisions. Confusions often arise in this process, because there often exist substantial discrepancies in customer reviews across different retailers on the same product. Clarifying such confusions can help consumers reduce concerns to make up their mind for their purchases, therefore benefiting both consumers and retailers. Through text analytics and sentiment analysis, we comparatively examine the underlying patterns of online consumer reviews of three large retailers including Sears, Home Depot, and Best Buy for a same product. Afterward, we combine online consumer reviews from these large retailers and conduct an overall text analytics and sentiment analysis. The overall results are further compared with the results from individual retailers. The findings show that the sentiment of the online consumer reviews could vary substantially so relying on a single data source to make purchase decision is not a wise idea. Based on the results, we further devise a framework to comparatively examine and integrate multiple data sources for social media analytics of online consumer reviews. This study offers important managerial implications and identifies several new research directions for social media analytics.     

The Effects of Credible Online Reviews on Brand Equity Dimensions and Its Consequence on Consumer Behavior

Consumers are using social media platform to gain and share knowledge on brands. In the virtual environment, consumers are exposed to various online reviews on brands that leave an impression of brands on the minds of the consumers. The present study combines Yale attitude change model and attribution theory to examine the effects of credible online reviews on brand equity dimensions. The present study views, through the lens of Yale attitude change model, the various factors that affect credibility evaluation of online reviews. Further, attribution theory is used as the theoretical backbone to analyze the effects of credible online reviews on brand equity dimensions and finally on purchase intention. This study uses structural equations modeling (SEM) to investigate the impact of online credible reviews on customer based brand equity (CBBE) dimensions and its consequence on consumer behavior (purchase intention). Results indicate that source and review quality are the most important factors that affect consumer's credibility evaluation of a review. Online credible reviews have more significant impact on brand awareness, perceived value and organizational associations and thus leads to consumer's purchase intention in the context of consumer electronic products in India.